Abstract
Summary Nonlinear beamforming is an effective method to enhance the quality of noisy seismic data. It uses local traveltime operators to describe local wavefronts, and then stack neighboring traces guided by these operators to enhance data quality. The 2+2+1 method is a pragmatic solver for estimating local traveltime operators from input data, but its calculation efficiency is not satisfying when the solution space is big. We speed up the 2+2+1 method using graphics processing unit (GPU) computing with the Compute Unified Device Architecture (CUDA) programming language. We introduce our GPU-based 2+2+1 algorithm, and demonstrate its efficiency improvement using a field data example. A speed-up factor of ∼10 is obtained compared to the CPU version of the 2+2+1 method.
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